The present disclosure relates to face image processing technologies, and in particular to a method and a system for extracting a characteristic of a three-dimensional face image.
Face recognition and emotion analysis are two important branches in a biological characteristic recognition system, and are widely applied to remote communications, medical rescue and intelligent monitoring. In conventional technologies, extraction of a characteristic of a three-dimensional face image generally merely satisfies face recognition or emotion analysis individually. For example, for three-dimensional face recognition, in the conventional technologies, a face is divided into a group of areas, by individually selecting and matching different areas, result fusion is performed to improve recognition performance; also, large posture conversion is overcome by using face symmetry, and an automatic landmark detector is provided to estimate a posture and detect a sheltering area. However, these methods pay more attention to face recognition rather than face expression description. For face expression description, in the conventional technologies, a face action coding system is used as a face expression indication and used for face expression analysis, but the method is concerned with face expression description, and cannot distinguish different individuals.
However, more and more practices require that face recognition or emotion analysis is not satisfied individually, but both individuals and expressions need to be distinguished, that is, both face recognition and emotion analysis need to be distinguished.